The present invention relates generally to the field of traffic control, and more particularly to controlling a traffic signal through cognitive computing that incorporates real time data at an intersection.
Traffic lights, also known as traffic signals, traffic lamps, traffic semaphore, signal lights, stop lights, robots, and traffic control signals, are signaling devices positioned at road intersections, pedestrian crossings, and other locations to control flows of traffic. The normal function of traffic lights requires control and coordination to ensure that traffic moves smoothly and safely. Traffic light controls include fixed time control, dynamic control, and adaptive traffic control. Fixed time controls are electro-mechanical signal controllers utilizing dial timers (e.g., cycle gears) with fixed, signalized intersection time plans that sometimes range from 35 seconds to 120 seconds in length and in which the timing does not change throughout the day. Dynamic control or traffic signal preemption uses input from detectors (e.g., in-pavement detectors, non-intrusive detectors, and non-motorized user detection), which are sensors that inform the controller processor whether vehicles or other road users are present, to adjust signal timing and phasing within the limits set by the controller's programming. In-pavement detectors are sensors buried in the road to detect the presence of traffic waiting at the light, that default to a timer when traffic is not present and/or low density. Non-intrusive detectors include video image processors, sensors that use electromagnetic waves, or acoustic sensors to detect the presence of vehicles at the intersection waiting for right of way. Non-motorized user detection is present at some traffic control signals and includes a button that can be pressed to activate the timing system. Coordinated control systems utilize a master controller in which the traffic lights cascade in a sequence such that a vehicle encounters a continuous series of green lights. Adaptive traffic control is a traffic management strategy in which traffic signal timing changes, or adapts, based on actual traffic demand.
Computer vision utilizes computers to gain high-level understanding from digital images or videos. Computer vision encompasses acquiring, processing, analyzing and understanding digital images, and extracts high-dimensional data to produce numerical or symbolic information in the forms of decisions. Sub-domains of computer vision include scene reconstruction, event detection, video tracking, object recognition (i.e. identifying objects in an image or video sequence), object pose estimation, learning, indexing, motion estimation (i.e., transformation from one 2D image to a second 2D image), and image restoration. Object recognition includes appearance based methods and feature based methods. Appearance based methods use example images, templates, or exemplars to perform recognition (e.g., edge matching, divide and conquer search, greyscale matching, gradient matching, histograms, and large model bases, etc.). Feature based methods search for feasible matches between object features and image features by extracting features from objects to be recognized with respect to the searched images (e.g., interpretation trees, hypothesize and test, pose consistency, pose clustering, invariance, geometric hashing, scale invariant feature transform, sped up robust features, etc.).